174 lines
4.9 KiB
Python
174 lines
4.9 KiB
Python
import argparse
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import asyncio
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import json
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import os
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import time
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from concurrent.futures import ThreadPoolExecutor
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import numpy as np
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import pandas as pd
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import tiktoken
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from tqdm import tqdm
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from sglang.test.test_utils import add_common_other_args_and_parse, get_call_generate
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choices = ["A", "B", "C", "D"]
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tokenizer = tiktoken.encoding_for_model("gpt-3.5-turbo")
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def format_subject(subject):
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l = subject.split("_")
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s = ""
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for entry in l:
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s += " " + entry
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return s
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def format_example(df, idx, include_answer=True):
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prompt = df.iloc[idx, 0]
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k = df.shape[1] - 2
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for j in range(k):
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prompt += "\n{}. {}".format(choices[j], df.iloc[idx, j + 1])
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prompt += "\nAnswer:"
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if include_answer:
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prompt += " {}\n\n".format(df.iloc[idx, k + 1])
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return prompt
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def gen_prompt(train_df, subject, k=-1):
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prompt = "The following are multiple choice questions (with answers) about{}.\n\n".format(
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format_subject(subject)
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)
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if k == -1:
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k = train_df.shape[0]
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for i in range(k):
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prompt += format_example(train_df, i)
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return prompt
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def evaluate(args, subject, dev_df, test_df, call_generate):
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prompts = []
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labels = []
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# Construct prompts
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k = args.ntrain
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train_prompt = gen_prompt(dev_df, subject, k)
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while len(tokenizer.encode(train_prompt)) > 1536:
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k -= 1
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train_prompt = gen_prompt(dev_df, subject, k)
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for i in range(test_df.shape[0]):
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prompt_end = format_example(test_df, i, include_answer=False)
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prompt = train_prompt + prompt_end
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prompts.append(prompt)
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label = test_df.iloc[i, test_df.shape[1] - 1]
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labels.append(label)
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preds = [None] * len(prompts)
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max_tokens = 1
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# Run requests
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if args.backend != "lmql":
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# Use thread pool
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def get_one_answer(i):
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pred = call_generate(prompts[i], temperature=0, max_tokens=max_tokens)
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preds[i] = pred.strip()[0]
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tic = time.perf_counter()
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if args.parallel == 1:
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for i in range(len(prompts)):
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get_one_answer(i)
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else:
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with ThreadPoolExecutor(args.parallel) as executor:
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executor.map(get_one_answer, list(range(len(prompts))))
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else:
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# Use asyncio
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async def batched_call(batch_size):
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for i in range(0, len(prompts), batch_size):
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tasks = []
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for p in prompts[i : i + batch_size]:
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tasks.append(call_generate(p, temperature=0, max_tokens=max_tokens))
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rets = await asyncio.gather(*tasks)
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for j in range(len(rets)):
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preds[i + j] = rets[j].strip()[0]
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tic = time.perf_counter()
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asyncio.run(batched_call(batch_size=args.parallel))
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latency = time.perf_counter() - tic
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# Compute accuracy
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cors = [pred == label for pred, label in zip(preds, labels)]
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acc = np.mean(cors)
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cors = np.array(cors)
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print(
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"Average accuracy {:.3f}, latency {:.2f}, #q: {} - {}".format(
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acc, latency, len(prompts), subject
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)
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)
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return cors, acc, latency
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def main(args):
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subjects = sorted(
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[
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f.split("_test.csv")[0]
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for f in os.listdir(os.path.join(args.data_dir, "test"))
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if "_test.csv" in f
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]
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)
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all_cors = []
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all_latencies = []
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num_requests = 0
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# Select backend
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call_generate = get_call_generate(args)
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for subject in tqdm(subjects[: args.nsub]):
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dev_df = pd.read_csv(
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os.path.join(args.data_dir, "dev", subject + "_dev.csv"), header=None
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)[: args.ntrain]
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test_df = pd.read_csv(
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os.path.join(args.data_dir, "test", subject + "_test.csv"), header=None
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)
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cors, acc, latency = evaluate(args, subject, dev_df, test_df, call_generate)
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all_cors.append(cors)
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all_latencies.append(latency)
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num_requests += len(test_df)
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total_latency = np.sum(all_latencies)
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print("Total latency: {:.3f}".format(total_latency))
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weighted_acc = np.mean(np.concatenate(all_cors))
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print("Average accuracy: {:.3f}".format(weighted_acc))
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# Write results
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with open(args.result_file, "a") as fout:
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value = {
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"task": "mmlu",
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"backend": args.backend,
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"num_gpus": 1,
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"latency": round(total_latency, 3),
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"accuracy": round(weighted_acc, 3),
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"num_requests": num_requests,
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"other": {
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"nsub": args.nsub,
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"parallel": args.parallel,
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},
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}
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fout.write(json.dumps(value) + "\n")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--ntrain", type=int, default=5)
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parser.add_argument("--data_dir", type=str, default="data")
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parser.add_argument("--nsub", type=int, default=60)
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args = add_common_other_args_and_parse(parser)
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main(args)
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